MicroRNA- and mRNA-based studies in paraffin-archived human osteosarcoma specimens reveal profiles with reproducible and independent prognostic value
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Katherine Hill
PROVIDER: E-GEOD-39058 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Osteosarcoma is a malignancy with variable outcomes that are not tightly aligned with chemotherapy response. Previous work has suggested a possible role for microRNAs (miRNAs) in determining the susceptibility of osteosarcomas to treatment but formal miRNA-based outcome models have not been reported. Formalin-fixed, paraffin-embedded (FFPE) specimens may be a unique approach for such studies in a rare malignancy. We used supervised principal components analysis and logistic regression to study survival and chemoresponse endpoints, and miRNA activity and gene set analysis algorithms to study miRNA regulatory networks using mRNA data. Our study also creates a paradigm for FFPE-based miRNA clinical biomarker research in rare tumors. 65 unique diagnostic biopsy specimens were profiled. Samples with paired surgical resection specimens (not included in this submission) are denoted in the Sample 'characteristics' field.
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Project description:Osteosarcoma is a malignancy with variable outcomes that are not tightly aligned with chemotherapy response. Previous work has suggested a possible role for microRNAs (miRNAs) in determining the susceptibility of osteosarcomas to treatment but formal miRNA-based outcome models have not been reported. Formalin-fixed, paraffin-embedded (FFPE) specimens may be a unique approach for such studies in a rare malignancy. We used supervised principal components analysis and logistic regression to study survival and chemoresponse endpoints, and miRNA activity and gene set analysis algorithms to study miRNA regulatory networks using mRNA data. Our study also creates a paradigm for FFPE-based miRNA clinical biomarker research in rare tumors. 26 unique pairs of biopsy and surgical resection specimens were profiled. Paired samples are denoted in the Sample 'characteristics' field.
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Project description:Osteosarcoma is a malignancy with variable outcomes that are not tightly aligned with chemotherapy response. Previous work has suggested a possible role for microRNAs (miRNAs) in determining the susceptibility of osteosarcomas to treatment but formal miRNA-based outcome models have not been reported. Formalin-fixed, paraffin-embedded (FFPE) specimens may be a unique approach for such studies in a rare malignancy. We used supervised principal components analysis and logistic regression to study survival and chemoresponse endpoints, and miRNA activity and gene set analysis algorithms to study miRNA regulatory networks using mRNA data. Our study also creates a paradigm for FFPE-based miRNA clinical biomarker research in rare tumors. 37 unique diagnostic biopsy specimens were profiled. Samples with paired surgical resection specimens (not included in this submission) are denoted in the Sample 'characteristics' field.
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Project description:Osteosarcoma is a malignancy with variable outcomes that are not tightly aligned with chemotherapy response. Previous work has suggested a possible role for microRNAs (miRNAs) in determining the susceptibility of osteosarcomas to treatment but formal miRNA-based outcome models have not been reported. Formalin-fixed, paraffin-embedded (FFPE) specimens may be a unique approach for such studies in a rare malignancy. We used supervised principal components analysis and logistic regression to study survival and chemoresponse endpoints, and miRNA activity and gene set analysis algorithms to study miRNA regulatory networks using mRNA data. Our study also creates a paradigm for FFPE-based miRNA clinical biomarker research in rare tumors. 5 unique pairs of diagnostic biopsy and surgical resection specimens were profiled. Paired samples are denoted in the Sample 'characteristics' field.
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Project description:Each cell type responds uniquely to stress and fractionally contributes to global and tissue-specific stress responses. Hepatocytes, liver macrophages (M?), and sinusoidal endothelial cells (SEC) play functionally important and interdependent roles in adaptive processes such as wound healing, obesity, and tumor growth. Although these cell types demonstrate significant phenotypic and functional heterogeneity, their distinctions enabling disease-specific responses remain understudied. To address this, we developed a strategy for simultaneous isolation and quantification of these liver cell types based on antigenic cell surface marker expression in response to DEN and found that while there was only a marginal increase in hepatocyte number, M? and SEC populations were quantitatively increased. Global gene expression profiling of hepatocytes, M? and SEC identified characteristic gene “fingerprints” that define each cell type and their distinct physiological or oncogenic stress signatures. Integration of these cell-specific gene fingerprints with available hepatocellular carcinoma (HCC) patient microarray data demonstrates that the hepatocyte-specific response strongly correlates with the human HCC gene expression profile. Liver-specific M? and SEC gene signatures demonstrate significant alterations in inflammatory and angiogenic gene regulatory pathways, which may impact the hepatocyte response to oncogenic stress. Further validation confirms alterations in components of two key pathways, AP-1 and p53, that have been previously associated with HCC onset and progression. Our data reveal unique gene expression patterns that serve as molecular “fingerprints” for the cell-centric responses to pathologic stimuli in the distinct microenvironment of the liver. The technical advance highlighted in this study provides an essential resource for assessing hepatic cell-specific contributions to oncogenic stress, information that could unveil previously unappreciated molecular mechanisms for the cellular crosstalk that underlies the development of hepatic cancer. Total RNA isolated from each specific cell type (Hepatocyte (PH), Sinusoidal Endothelial Cell (SEC), and Macrophage (MP)) pooled from 3-4 DEN-treated mice and compared to their untreated counterparts.
2015-09-20 | E-GEOD-67225 | biostudies-arrayexpress
Project description:Conventional high-grade osteosarcoma is a primary malignant bone tumor, which is most prevalent in adolescence. Survival rates of osteosarcoma patients have not improved significantly in the last 25 years. Aiming to increase this survival rate, a variety of model systems are used to study osteosarcomagenesis and to test new therapeutic agents. Such model systems are typically generated from an osteosarcoma primary tumor, but undergo many changes due to culturing or interactions with a different host species, which may result into differences in gene expression between primary tumor cells, and tumor cells from the model system. We aimed to investigate whether gene expression profiles of osteosarcoma cell lines and xenografts are still comparable to those of the primary tumor. Osteosarcoma can be subdivided into several histological subtypes, of which osteoblastic, chondroblastic, and fibroblastic osteosarcoma are the most frequent ones. Using nearest shrunken centroids classification, we have generated an expression profile that can predict these histological subtypes in both osteosarcoma biopsies (n=66), as well as in two osteosarcoma model systems, i.e. osteosarcoma cell lines (n=13) and xenografts (n=18). Based on the preservation of mRNA expression profiles that are characteristic for the histological subtype we propose that these model systems are representative to the primary tumor from which they are derived. Genome-wide expression profiling was performed on pre-treatment diagnostic biopsies of 76 resectable high-grade osteosarcoma patients from the EuroBoNet consortium (www.eurobonet.eu). Samples with a main histological subtype (n=66) were selected for subsequent subtype analyses. Out of the EuroBoNeT panel of 19 cell lines, 13 cell lines were recorded to belong to a main histological subtype. This set of 13 cell lines contained 4 cell lines derived from fibroblastic, and 9 cell lines derived from osteoblastic osteosarcomas. The 13 osteosarcoma cell lines IOR/MOS, IOR/OS10, IOR/OS14, IOR/OS15, IOR/OS18, IOR/OS9, IOR/SARG, KPD, MG-63, MHM, OHS, OSA, and ZK-58 were maintained in RPMI 1640 (Invitrogen, Carlsbad, CA, USA) supplemented with 10% fetal calf serum and 1% Penicillin/Streptomycin (Invitrogen) as previously described (PMID: 19787792). The osteosarcoma xenograft model is described in Kresse et al. (PMID: 21713766) In short, human tumors were implanted directly from patient samples and successively passaged subcutaneously in nude mice. Eighteen different xenografts were used, of which 3 were derived from chondroblastic, and 15 from osteoblastic osteosarcomas. Osteosarcoma and xenograft tissue was handled as previously described in Buddingh, Kuijjer et al. (PMID: 21372215) Osteosarcoma cell lines were prepared as in Ottaviano et al. (PMID: 19787792) RNA isolation, synthesis of cDNA, cRNA amplification, and hybridization of cRNA onto the Illumina Human-6 v2.0 Expression BeadChips were performed as described in Buddingh, Kuijjer, et al. (PMID: 21372215) Microarray data processing and quality control were performed using the statistical language R as described in Buddingh, Kuijjer, et al. (PMID: 21372215) We performed a factorial LIMMA analysis comparing 50 osteoblastic, 9 chondroblastic, and 7 fibroblastic osteosarcomas pre-chemotherapy biopsies. Probes with Benjamini and Hochberg False discovery rate-adjusted P-values < 0.05 were considered to be significantly differentially expressed. The gene expression profile was generated on the dataset of biopsies using Bioconductor package PAMR. Internal cross-validation was performed 50 times. A threshold was selected where the error rate of the prediction profile was minimal. The minimum error rate was representative of 50 independent simulations. In order to minimize optimization bias, we validated the profile on an independent dataset of osteosarcoma biopsies (n=5), containing 1 chondroblastic osteosarcoma and 4 osteoblastic osteosarcomas. We subsequently applied the validated prediction profile to two independent datasets, the first consisting of gene expression data of osteosarcoma cell lines, the second of xenografts. Expression of the probes that composed the prediction profile was verified using a factorial LIMMA analysis, comparing chondroblastic, fibroblastic, and osteoblastic osteosarcoma biopsy samples.
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